Overview

Dataset statistics

Number of variables10
Number of observations339396
Missing cells308645
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.5 MiB
Average record size in memory88.0 B

Variable types

Numeric10

Alerts

EC is highly overall correlated with DOHigh correlation
DO is highly overall correlated with EC and 1 other fieldsHigh correlation
Temp is highly overall correlated with DOHigh correlation
pH has 48498 (14.3%) missing valuesMissing
EC has 106381 (31.3%) missing valuesMissing
DO has 48728 (14.4%) missing valuesMissing
TP has 5581 (1.6%) missing valuesMissing
ORP has 47230 (13.9%) missing valuesMissing
Temp has 49294 (14.5%) missing valuesMissing

Reproduction

Analysis started2023-03-04 15:13:00.003949
Analysis finished2023-03-04 15:13:30.418453
Duration30.41 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

pH
Real number (ℝ)

Distinct1164
Distinct (%)0.4%
Missing48498
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean7.3803322
Minimum0.11
Maximum13.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T22:13:30.588535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile5.06
Q16.32
median7.13
Q37.93
95-th percentile11.55
Maximum13.03
Range12.92
Interquartile range (IQR)1.61

Descriptive statistics

Standard deviation1.8726389
Coefficient of variation (CV)0.25373369
Kurtosis1.1175093
Mean7.3803322
Median Absolute Deviation (MAD)0.8
Skewness0.6796879
Sum2146923.9
Variance3.5067764
MonotonicityNot monotonic
2023-03-04T22:13:30.788948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.32 1561
 
0.5%
7.4 1554
 
0.5%
7.31 1527
 
0.4%
7.3 1486
 
0.4%
7.33 1461
 
0.4%
7.39 1460
 
0.4%
7.34 1444
 
0.4%
7.37 1439
 
0.4%
7.35 1415
 
0.4%
7.36 1409
 
0.4%
Other values (1154) 276142
81.4%
(Missing) 48498
 
14.3%
ValueCountFrequency (%)
0.11 3
 
< 0.1%
0.12 3
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.17 2
 
< 0.1%
0.18 5
 
< 0.1%
0.19 2
 
< 0.1%
0.21 9
 
< 0.1%
0.24 11
 
< 0.1%
0.25 29
< 0.1%
ValueCountFrequency (%)
13.03 1
 
< 0.1%
13.02 3
< 0.1%
13.01 4
< 0.1%
13 2
 
< 0.1%
12.99 2
 
< 0.1%
12.98 3
< 0.1%
12.97 1
 
< 0.1%
12.95 2
 
< 0.1%
12.94 4
< 0.1%
12.93 6
< 0.1%

EC
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87864
Distinct (%)37.7%
Missing106381
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean24881.313
Minimum0.01
Maximum49999.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T22:13:30.959594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile8.34
Q11000
median32506.48
Q342105.73
95-th percentile46551.57
Maximum49999.48
Range49999.47
Interquartile range (IQR)41105.73

Descriptive statistics

Standard deviation18942.75
Coefficient of variation (CV)0.76132439
Kurtosis-1.6732361
Mean24881.313
Median Absolute Deviation (MAD)12514.28
Skewness-0.30116365
Sum5.7977191 × 109
Variance3.5882779 × 108
MonotonicityNot monotonic
2023-03-04T22:13:31.129992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.38 6502
 
1.9%
6.25 5249
 
1.5%
15.62 4142
 
1.2%
1000 3682
 
1.1%
12.5 2808
 
0.8%
3.13 2525
 
0.7%
18.75 1317
 
0.4%
25 609
 
0.2%
10.42 403
 
0.1%
13.54 368
 
0.1%
Other values (87854) 205410
60.5%
(Missing) 106381
31.3%
ValueCountFrequency (%)
0.01 40
 
< 0.1%
0.03 1
 
< 0.1%
0.11 1
 
< 0.1%
0.23 1
 
< 0.1%
0.52 170
0.1%
0.53 4
 
< 0.1%
0.63 22
 
< 0.1%
0.78 54
 
< 0.1%
0.79 2
 
< 0.1%
0.96 1
 
< 0.1%
ValueCountFrequency (%)
49999.48 3
< 0.1%
49998.96 2
< 0.1%
49997.92 1
 
< 0.1%
49997.4 1
 
< 0.1%
49996.88 1
 
< 0.1%
49996.87 1
 
< 0.1%
49996.36 1
 
< 0.1%
49996.35 2
< 0.1%
49995.83 1
 
< 0.1%
49995.01 1
 
< 0.1%

DO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct962
Distinct (%)0.3%
Missing48728
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean5.9062766
Minimum0.01
Maximum11.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T22:13:31.265815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.14
Q14.02
median6.8
Q37.57
95-th percentile8.29
Maximum11.92
Range11.91
Interquartile range (IQR)3.55

Descriptive statistics

Standard deviation2.1125189
Coefficient of variation (CV)0.35767355
Kurtosis-0.49999186
Mean5.9062766
Median Absolute Deviation (MAD)1.19
Skewness-0.76220919
Sum1716765.6
Variance4.4627361
MonotonicityNot monotonic
2023-03-04T22:13:31.406604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 3115
 
0.9%
7.12 1155
 
0.3%
7.11 1153
 
0.3%
7.21 1142
 
0.3%
7.22 1129
 
0.3%
7.19 1116
 
0.3%
7.1 1105
 
0.3%
7.2 1100
 
0.3%
7.14 1099
 
0.3%
7.23 1091
 
0.3%
Other values (952) 277463
81.8%
(Missing) 48728
 
14.4%
ValueCountFrequency (%)
0.01 3115
0.9%
0.02 188
 
0.1%
0.03 65
 
< 0.1%
0.04 4
 
< 0.1%
0.05 2
 
< 0.1%
0.06 5
 
< 0.1%
0.07 5
 
< 0.1%
0.08 2
 
< 0.1%
0.09 5
 
< 0.1%
0.1 3
 
< 0.1%
ValueCountFrequency (%)
11.92 1
< 0.1%
11.02 2
< 0.1%
10.55 1
< 0.1%
10.38 1
< 0.1%
10.21 1
< 0.1%
10 1
< 0.1%
9.95 1
< 0.1%
9.9 1
< 0.1%
9.83 1
< 0.1%
9.8 1
< 0.1%

TSS
Real number (ℝ)

Distinct207463
Distinct (%)61.3%
Missing1037
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean105.63883
Minimum0.0060457831
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T22:13:31.555570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0060457831
5-th percentile1.51
Q114.03
median56.619498
Q3156.93623
95-th percentile335.49095
Maximum999
Range998.99395
Interquartile range (IQR)142.90623

Descriptive statistics

Standard deviation135.24683
Coefficient of variation (CV)1.2802758
Kurtosis13.63263
Mean105.63883
Median Absolute Deviation (MAD)50.279498
Skewness2.930879
Sum35743850
Variance18291.706
MonotonicityNot monotonic
2023-03-04T22:13:31.755989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.44 2654
 
0.8%
999 2534
 
0.7%
1.56 1959
 
0.6%
1.38 1823
 
0.5%
1.06 1626
 
0.5%
1.12 988
 
0.3%
1.5 931
 
0.3%
1.62 899
 
0.3%
1.25 783
 
0.2%
0.94 625
 
0.2%
Other values (207453) 323537
95.3%
(Missing) 1037
 
0.3%
ValueCountFrequency (%)
0.006045783134 1
 
< 0.1%
0.01 33
< 0.1%
0.01154646053 1
 
< 0.1%
0.01435289532 1
 
< 0.1%
0.01593086682 1
 
< 0.1%
0.01782574825 1
 
< 0.1%
0.01926957067 1
 
< 0.1%
0.02 45
< 0.1%
0.02117326872 1
 
< 0.1%
0.02270605168 1
 
< 0.1%
ValueCountFrequency (%)
999 2534
0.7%
998.77 1
 
< 0.1%
956.75 1
 
< 0.1%
934.91 1
 
< 0.1%
885.63 1
 
< 0.1%
880.96 1
 
< 0.1%
878.8112226 1
 
< 0.1%
851.02 1
 
< 0.1%
804.37 1
 
< 0.1%
792.64 1
 
< 0.1%

TN
Real number (ℝ)

Distinct329677
Distinct (%)97.2%
Missing59
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.60177
Minimum0.00019439124
Maximum14.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T22:13:31.917602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00019439124
5-th percentile0.84913211
Q12.3539408
median3.5342378
Q34.7514784
95-th percentile6.5671114
Maximum14.89
Range14.889806
Interquartile range (IQR)2.3975376

Descriptive statistics

Standard deviation1.7265983
Coefficient of variation (CV)0.47937494
Kurtosis-0.11285409
Mean3.60177
Median Absolute Deviation (MAD)1.1980038
Skewness0.29126033
Sum1222213.8
Variance2.9811416
MonotonicityNot monotonic
2023-03-04T22:13:32.077644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.51 186
 
0.1%
3.5 178
 
0.1%
3.56 149
 
< 0.1%
3.62 147
 
< 0.1%
3.46 141
 
< 0.1%
3.58 139
 
< 0.1%
3.44 136
 
< 0.1%
3.25 129
 
< 0.1%
3.57 123
 
< 0.1%
3.54 122
 
< 0.1%
Other values (329667) 337887
99.6%
ValueCountFrequency (%)
0.0001943912385 1
< 0.1%
0.0004321799478 1
< 0.1%
0.000520341101 1
< 0.1%
0.0006229090311 1
< 0.1%
0.0007151136787 1
< 0.1%
0.0008995685182 1
< 0.1%
0.001294563841 1
< 0.1%
0.001373222761 1
< 0.1%
0.001631580413 1
< 0.1%
0.001644971519 1
< 0.1%
ValueCountFrequency (%)
14.89 12
< 0.1%
13.79 3
 
< 0.1%
13.67 1
 
< 0.1%
12.91 1
 
< 0.1%
12.65 1
 
< 0.1%
12.42 1
 
< 0.1%
11.77 1
 
< 0.1%
11.75168487 1
 
< 0.1%
11.73987027 1
 
< 0.1%
11.61 1
 
< 0.1%

TP
Real number (ℝ)

Distinct328916
Distinct (%)98.5%
Missing5581
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean73.526732
Minimum0.00011197593
Maximum435.33311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T22:13:32.240899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00011197593
5-th percentile4.1293786
Q128.750815
median62.441226
Q3106.75159
95-th percentile181.38455
Maximum435.33311
Range435.333
Interquartile range (IQR)78.00078

Descriptive statistics

Standard deviation56.139824
Coefficient of variation (CV)0.76352943
Kurtosis0.79262742
Mean73.526732
Median Absolute Deviation (MAD)37.544907
Skewness0.96581308
Sum24544326
Variance3151.6798
MonotonicityNot monotonic
2023-03-04T22:13:32.372587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 809
 
0.2%
0.01 611
 
0.2%
0.03 463
 
0.1%
0.05 217
 
0.1%
0.04 199
 
0.1%
0.07 159
 
< 0.1%
0.06 105
 
< 0.1%
0.1 94
 
< 0.1%
0.11 87
 
< 0.1%
0.41 66
 
< 0.1%
Other values (328906) 331005
97.5%
(Missing) 5581
 
1.6%
ValueCountFrequency (%)
0.0001119759281 1
< 0.1%
0.0002522438245 1
< 0.1%
0.000351450711 1
< 0.1%
0.0004413982976 1
< 0.1%
0.0005256530384 1
< 0.1%
0.0007113401034 1
< 0.1%
0.0008474424665 1
< 0.1%
0.001354511027 1
< 0.1%
0.001925187992 1
< 0.1%
0.002084801125 1
< 0.1%
ValueCountFrequency (%)
435.3331096 1
< 0.1%
427.2802599 1
< 0.1%
395.7568261 1
< 0.1%
393.6419867 1
< 0.1%
391.7617474 1
< 0.1%
387.4939108 1
< 0.1%
387.0504534 1
< 0.1%
386.6448199 1
< 0.1%
386.4961369 1
< 0.1%
384.6805697 1
< 0.1%

TOC
Real number (ℝ)

Distinct336541
Distinct (%)99.7%
Missing1837
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean18.695457
Minimum0.00011462002
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T22:13:32.532805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00011462002
5-th percentile3.5507711
Q111.610439
median18.482246
Q325.552804
95-th percentile34.645857
Maximum40
Range39.999885
Interquartile range (IQR)13.942365

Descriptive statistics

Standard deviation9.3343807
Coefficient of variation (CV)0.49928605
Kurtosis-0.74604311
Mean18.695457
Median Absolute Deviation (MAD)6.9670811
Skewness0.10277123
Sum6310819.6
Variance87.130662
MonotonicityNot monotonic
2023-03-04T22:13:32.752646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.05 82
 
< 0.1%
0.89 15
 
< 0.1%
3.86 13
 
< 0.1%
0.2 13
 
< 0.1%
0.76 12
 
< 0.1%
0.52 12
 
< 0.1%
0.79 10
 
< 0.1%
0.84 9
 
< 0.1%
0.13 9
 
< 0.1%
0.91 8
 
< 0.1%
Other values (336531) 337376
99.4%
(Missing) 1837
 
0.5%
ValueCountFrequency (%)
0.0001146200161 1
< 0.1%
0.0005736775409 1
< 0.1%
0.0005897045089 1
< 0.1%
0.0006757858735 1
< 0.1%
0.0009095416959 1
< 0.1%
0.00130479049 1
< 0.1%
0.001459512784 1
< 0.1%
0.001736374588 1
< 0.1%
0.002509600606 1
< 0.1%
0.003034235199 1
< 0.1%
ValueCountFrequency (%)
40 1
< 0.1%
39.99975382 1
< 0.1%
39.99972284 1
< 0.1%
39.99945558 1
< 0.1%
39.99908297 1
< 0.1%
39.9987846 1
< 0.1%
39.99859784 1
< 0.1%
39.9979189 1
< 0.1%
39.99775387 1
< 0.1%
39.99680354 1
< 0.1%

ORP
Real number (ℝ)

Distinct61273
Distinct (%)21.0%
Missing47230
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean363.68542
Minimum-1232.5
Maximum1000
Zeros1
Zeros (%)< 0.1%
Negative10031
Negative (%)3.0%
Memory size5.2 MiB
2023-03-04T22:13:32.935959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1232.5
5-th percentile72.4
Q1204.98
median342.185
Q3470.92
95-th percentile882.995
Maximum1000
Range2232.5
Interquartile range (IQR)265.94

Descriptive statistics

Standard deviation263.91407
Coefficient of variation (CV)0.72566578
Kurtosis1.2937239
Mean363.68542
Median Absolute Deviation (MAD)134.56
Skewness0.17763047
Sum1.0625652 × 108
Variance69650.634
MonotonicityNot monotonic
2023-03-04T22:13:33.081519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 7781
 
2.3%
89.38 84
 
< 0.1%
381.5 78
 
< 0.1%
88.62 76
 
< 0.1%
81.88 70
 
< 0.1%
88.75 66
 
< 0.1%
89.12 62
 
< 0.1%
509.12 61
 
< 0.1%
-443.12 61
 
< 0.1%
88.38 58
 
< 0.1%
Other values (61263) 283769
83.6%
(Missing) 47230
 
13.9%
ValueCountFrequency (%)
-1232.5 1
< 0.1%
-1200.25 1
< 0.1%
-1116.25 1
< 0.1%
-1002.2 1
< 0.1%
-973.58 1
< 0.1%
-905.73 1
< 0.1%
-835.6 1
< 0.1%
-653.81 1
< 0.1%
-527.1 1
< 0.1%
-511.62 1
< 0.1%
ValueCountFrequency (%)
1000 7781
2.3%
999.98 8
 
< 0.1%
999.96 10
 
< 0.1%
999.94 10
 
< 0.1%
999.92 3
 
< 0.1%
999.91 1
 
< 0.1%
999.9 3
 
< 0.1%
999.88 2
 
< 0.1%
999.87 3
 
< 0.1%
999.85 5
 
< 0.1%

Temp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2252
Distinct (%)0.8%
Missing49294
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean27.540814
Minimum3.61
Maximum59.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T22:13:33.223722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.61
5-th percentile21.72
Q124.31
median27.1
Q330.83
95-th percentile33.55
Maximum59.39
Range55.78
Interquartile range (IQR)6.52

Descriptive statistics

Standard deviation3.9047639
Coefficient of variation (CV)0.14178099
Kurtosis-0.16871435
Mean27.540814
Median Absolute Deviation (MAD)3.21
Skewness0.29135135
Sum7989645.3
Variance15.247181
MonotonicityNot monotonic
2023-03-04T22:13:33.364340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.6 950
 
0.3%
25.9 933
 
0.3%
21.62 916
 
0.3%
25.44 873
 
0.3%
24.97 855
 
0.3%
22.1 848
 
0.2%
24.12 778
 
0.2%
23.56 755
 
0.2%
24.96 737
 
0.2%
23.54 723
 
0.2%
Other values (2242) 281734
83.0%
(Missing) 49294
 
14.5%
ValueCountFrequency (%)
3.61 1
< 0.1%
4.9 1
< 0.1%
5.43 1
< 0.1%
8.51 1
< 0.1%
8.79 1
< 0.1%
10.85 1
< 0.1%
12.39 1
< 0.1%
13.56 1
< 0.1%
14.86 1
< 0.1%
14.92 1
< 0.1%
ValueCountFrequency (%)
59.39 1
< 0.1%
59.28 1
< 0.1%
59.14 1
< 0.1%
59.13 1
< 0.1%
58.96 1
< 0.1%
58.87 1
< 0.1%
58.85 1
< 0.1%
58.8 1
< 0.1%
58.72 1
< 0.1%
58.68 1
< 0.1%

TEMP
Real number (ℝ)

Distinct338319
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.89239
Minimum21.18
Maximum39.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2023-03-04T22:13:33.514915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum21.18
5-th percentile23.535954
Q127.112945
median29.852345
Q332.627631
95-th percentile36.382148
Maximum39.33
Range18.15
Interquartile range (IQR)5.5146861

Descriptive statistics

Standard deviation3.8413333
Coefficient of variation (CV)0.12850539
Kurtosis-0.55924557
Mean29.89239
Median Absolute Deviation (MAD)2.7583043
Skewness0.05588391
Sum10145357
Variance14.755842
MonotonicityNot monotonic
2023-03-04T22:13:33.664407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.26 13
 
< 0.1%
24.98 10
 
< 0.1%
29.74 9
 
< 0.1%
25 9
 
< 0.1%
35 9
 
< 0.1%
31.86 9
 
< 0.1%
35.88 9
 
< 0.1%
28.93 8
 
< 0.1%
35.24 8
 
< 0.1%
36.38 8
 
< 0.1%
Other values (338309) 339304
> 99.9%
ValueCountFrequency (%)
21.18 1
< 0.1%
21.1803509 1
< 0.1%
21.18074945 1
< 0.1%
21.18089344 1
< 0.1%
21.18111111 1
< 0.1%
21.18124418 1
< 0.1%
21.18134117 1
< 0.1%
21.182297 1
< 0.1%
21.18244275 1
< 0.1%
21.18266495 1
< 0.1%
ValueCountFrequency (%)
39.33 1
< 0.1%
39.32987303 1
< 0.1%
39.3294811 1
< 0.1%
39.32932315 1
< 0.1%
39.32863458 1
< 0.1%
39.32716928 1
< 0.1%
39.32649945 1
< 0.1%
39.32630934 1
< 0.1%
39.32611579 1
< 0.1%
39.32606748 1
< 0.1%

Interactions

2023-03-04T22:13:26.959879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:07.680871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:09.696622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:11.602709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:13.619137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:16.496138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:18.655023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:20.806101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:22.970763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:24.962874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:27.143775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:07.904023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:09.881911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:11.791231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:13.799002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:16.681042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:18.831125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:21.002610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:23.148028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:25.134336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:27.349552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:08.107239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:10.061271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:11.989588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:14.860580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:16.881787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:19.027182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:21.206751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:23.351107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:25.325558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:27.573723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:08.299664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:10.242663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:12.187557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:15.059878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:17.095971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:19.295419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:21.426588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:23.549835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:25.520416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:27.852794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:08.491784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:10.423060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:12.379910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:15.257510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:17.344103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:19.517133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:21.642090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:23.753495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:25.715205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:28.086497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:08.694607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:10.608493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:12.578419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:15.498869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:17.594274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:19.743716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:21.876825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:23.962662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:25.935276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:28.293322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:08.880857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:10.781919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:12.769852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:15.704961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:17.823297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:19.966073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:22.121634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:24.146167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:26.158459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:28.502808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:09.087250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:10.968822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:13.021832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:15.911473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:18.041499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:20.171182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:22.327832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:24.371702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:26.366103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:28.698060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:09.310253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:11.156162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:13.214226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:16.093385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:18.236132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:20.361445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:22.511505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:24.556642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:26.545962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:28.905246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:09.505758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:11.341617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:13.409455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:16.295610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:18.453009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:20.572745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:22.754604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:24.757766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-04T22:13:26.730007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-04T22:13:33.837164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
pHECDOTSSTNTPTOCORPTempTEMP
pH1.0000.0160.0960.056-0.003-0.008-0.003-0.3170.0710.002
EC0.0161.000-0.516-0.0370.0060.0110.0010.0200.322-0.002
DO0.096-0.5161.0000.076-0.0070.0080.002-0.155-0.5130.000
TSS0.056-0.0370.0761.0000.0020.0160.0000.026-0.128-0.001
TN-0.0030.006-0.0070.0021.0000.007-0.001-0.0030.003-0.004
TP-0.0080.0110.0080.0160.0071.000-0.003-0.007-0.0110.002
TOC-0.0030.0010.0020.000-0.001-0.0031.000-0.003-0.002-0.003
ORP-0.3170.020-0.1550.026-0.003-0.007-0.0031.0000.1850.002
Temp0.0710.322-0.513-0.1280.003-0.011-0.0020.1851.0000.001
TEMP0.002-0.0020.000-0.001-0.0040.002-0.0030.0020.0011.000

Missing values

2023-03-04T22:13:29.096035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-04T22:13:29.467291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-04T22:13:30.048145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

pHECDOTSSTNTPTOCORPTempTEMP
Time
2017-07-11 14:05:007.291000.00.0137.722.382600118.30176620.640303257.6232.6428.204791
2017-07-11 14:10:007.291000.00.0137.187.28457123.18287614.900992260.5032.5622.112042
2017-07-11 14:15:007.291000.00.0136.644.66897211.36309921.685466255.5432.5433.116497
2017-07-11 14:20:007.301000.00.0136.252.14671083.47461324.299291255.0632.4727.300682
2017-07-11 14:25:007.311000.00.0136.082.93431212.72558736.730378258.6232.4233.866263
2017-07-11 14:30:007.311000.00.0135.835.59466379.63950911.190794260.1032.4238.535924
2017-07-11 14:35:007.311000.00.0135.631.89190092.43836928.065020258.9232.4224.901455
2017-07-11 14:40:007.311000.00.0135.523.54802626.13504225.175644255.4232.3526.988880
2017-07-11 14:45:007.311000.00.0135.353.075528130.6204278.040837252.5232.3029.320082
2017-07-11 14:50:007.311000.00.0135.262.058291235.7046568.779740252.1232.2729.274177
pHECDOTSSTNTPTOCORPTempTEMP
Time
2020-10-02 00:15:0012.789.388.3086.5189214.04939256.54903321.018809384.3524.6430.558691
2020-10-02 00:20:0012.799.388.2966.9798483.946797155.5400439.907424383.5824.6728.146237
2020-10-02 00:25:0012.829.388.3096.2795054.82614134.11842421.399300384.1724.6936.198463
2020-10-02 00:30:0012.799.388.29133.5375014.77513537.84334221.170736383.8824.7531.043348
2020-10-02 00:35:0012.829.388.2782.9989045.80193384.53223713.370508383.3124.7627.773501
2020-10-02 00:40:0012.789.388.2891.4466543.20827162.40356715.988773384.2124.7634.115773
2020-10-02 00:45:0012.809.388.27209.7492301.39828347.29734738.411562383.6324.7626.268491
2020-10-02 00:50:0012.819.388.27201.6560976.552750145.28827511.178243384.0424.7627.838110
2020-10-02 00:55:0012.789.388.27176.8806135.85791380.2600971.642902384.0824.7736.598129
2020-10-02 01:00:0012.809.388.28168.4043212.81432126.74428627.944556384.2324.7638.210078